Abstract

Service Level Agreements (SLAs) specify the strict terms under which cloud services must be provided. The assessment of the quality of services being provided is critical for both clients and service providers. In this context, stakeholders must be capable of monitoring services delivered as Software as a Service (SaaS) at runtime and of reporting any eventual non-compliance with SLAs in a comprehensive and flexible manner. In this paper, we present the definition of an SLA compliance monitoring infrastructure, which is based on the use of models@run.time, its main components and artifacts, and the interactions among them. We place emphasis on the configuration of the artifacts that will enable the monitoring, and we present a prototype that can be used to perform this monitoring. The feasibility of our proposal is illustrated by means of a case study, which shows the use of the components and artifacts in the infrastructure and the configuration of a specific plan with which to monitor the services deployed on the Microsoft Azure© platform.

Recommended Citation

Cedillo, P., Gonzalez-Huerta, J., Abrahao, S., & Insfran, E. (2015). A Monitoring Infrastructure for the Quality Assessment of Cloud Services. In D. Vogel, X. Guo, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Transforming Healthcare through Information Systems (ISD2015 Proceedings). Hong Kong, SAR: Department of Information Systems. ISBN: 978-962-442-393-8. http://aisel.aisnet.org/isd2014/proceedings2015/MDDConcepts/1.

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A Monitoring Infrastructure for the Quality Assessment of Cloud Services

Service Level Agreements (SLAs) specify the strict terms under which cloud services must be provided. The assessment of the quality of services being provided is critical for both clients and service providers. In this context, stakeholders must be capable of monitoring services delivered as Software as a Service (SaaS) at runtime and of reporting any eventual non-compliance with SLAs in a comprehensive and flexible manner. In this paper, we present the definition of an SLA compliance monitoring infrastructure, which is based on the use of models@run.time, its main components and artifacts, and the interactions among them. We place emphasis on the configuration of the artifacts that will enable the monitoring, and we present a prototype that can be used to perform this monitoring. The feasibility of our proposal is illustrated by means of a case study, which shows the use of the components and artifacts in the infrastructure and the configuration of a specific plan with which to monitor the services deployed on the Microsoft Azure© platform.